AI Research Engineer (Multi-Modal & Vision)

3 months ago

Remote, WorldwideSenior

Responsibilities

Conduct end-to-end research and engineering for vision-language models across training, evaluation, and optimization.
Design and implement supervised fine-tuning, knowledge distillation, and reinforcement learning from human feedback pipelines.
Curate, filter, balance, and maintain high-quality multimodal datasets for domain-specific tasks.
Improve model efficiency and deployability for resource-constrained environments using compression and optimization techniques.
Build evaluation frameworks and benchmarks for performance, robustness, and real-world task success.
Build and scale training workflows across distributed GPU infrastructure and resolve pipeline bottlenecks.
Contribute to and leverage open-source models, datasets, and tooling.
Track multimodal learning research, apply relevant findings, and publish results in leading AI conferences and journals where applicable.

Degree in Computer Science, Machine Learning, or a related field; a master's or PhD is preferred.
Strong experience with multimodal post-training, including supervised fine-tuning, knowledge distillation, and reinforcement learning from feedback.
Hands-on experience with parameter-efficient fine-tuning and distributed training frameworks.
Demonstrated ability to build and improve vision-language models with measurable benchmark or real-world results.
Experience adapting models for resource-constrained environments.
Proven open-source contributions in multimodal AI on GitHub or Hugging Face.
Publications at top AI conferences such as NeurIPS, ICML, ICLR, CVPR, or ECCV.
Excellent English communication skills.

Remote work from locations around the world.
Opportunity to work with a small, high-caliber team on multimodal AI for real-world deployment.
Direct impact within Tether's fintech and digital-asset ecosystem.

201-500 employees